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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Nur Zawani Saharuddin
Universiti Teknikal Malaysia Melaka
Computer Science & IT Engineering

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Investigation of Wave Propagation to PV-Solar Panel Due to Lightning Induced Overvoltage Nur Hidayu Abdul Rahim; Zikri Abadi Baharudin; Md Nazri Othman; Zahriladha Zakaria; Mohd Shahril Ahmad Khiar; Nur Zawani Saharuddin; Azlinda Ahmad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 1: March 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i1.22

Abstract

Lightning produces extremely high voltages that generated induce overvoltage which have a high tendency to effect the electrical apparatus especially renewable energy plant that directly expose to this source. This study was performed through experimental work by investigating the effect of induce overvoltage upon the photovoltaic system. The induce voltage was performed by using lightning impulse generator. It is found that the maximum voltage of the unwanted signal is proportional with the distant of the specimen. The closer distant between solar panel material and spark discharge, the more serious effect would occur due to the induced overvoltage. 
Parkinsonian gait classification in older adults using time–frequency spectrograms and 2D convolutional neural network Kazi Ashikur Rahman; Ezreen Farina Shair; Nur Zawani Saharuddin; Muhammad Hazwan Adlin Jumaris
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2698-2708

Abstract

Gait disorders in adults aged 50 years and above are a common concern and are often linked to reduced mobility, a higher risk of falls, and a lower quality of life. This study presents a deep learning-based approach to detect gait disorders using vertical ground reaction force (vGRF) signals. The data were collected from older adults, including individuals with Parkinson’s disease (PD) and healthy controls, using force-sensitive resistor sensors. The raw signals were first processed using band-pass filtering and wavelet denoising to remove noise and unwanted variations. After that, the signals were converted into time–frequency representations using the continuous wavelet transform (CWT). These representations were then used as input to a convolutional neural network (CNN) for classification. The model achieved a validation accuracy of 93.48%, with precision, recall, and F1-score all above 92% for both groups. The results show that combining CWT with CNN provides a reliable and efficient way to detect gait disorders. This approach can support clinical evaluation by offering a practical and scalable method for analyzing gait patterns in older adults.
Co-Authors Azlinda Ahmad Ezreen Farina Shair Kazi Ashikur Rahman Md Nazri Othman Mohd Shahril Ahmad Khiar Muhammad Hazwan Adlin Jumaris Nur Hidayu Abdul Rahim Zahriladha Zakaria Zikri Abadi Baharudin